Supervised Semantic Classification for Nuclear Proliferation Monitoring
- ORNL
Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferation monitoring using high resolution remote sensing images.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 1015691
- Resource Relation:
- Conference: 39th IEEE Applied Imagery Pattern Recognition (AIPR) Workshop, Washington DC, DC, USA, 20101013, 20101015
- Country of Publication:
- United States
- Language:
- English
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